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Make AI speak your language.
Problem: Out-of-the-box LLMs often fall short for enterprises, struggling with industry-specific language, internal task accuracy, data privacy concerns, and the lack of fine-tuning infrastructure—making customization essential for meaningful performance on your data.
We define your fine-tuning goals classification, summarization, chat, RAG-enhanced responses—and curate or transform your proprietary data (PDFs, support logs, transcripts, reports) into high-quality training datasets.
We evaluate open-source models based on your needs (speed, size, licensing, context window) and optimize them with techniques like LoRA, QLoRA, and parameter-efficient tuning—minimizing compute without sacrificing performance.
We implement supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), and retrieval-augmented fine-tuning (RA-FT). Every model is rigorously evaluated across accuracy, relevance, and domain performance metrics.
Once fine-tuned, we help deploy your model using vLLM or similar frameworks. We also build pipelines for prompt logging, drift detection, human-in-the-loop feedback, and lightweight retraining—so your model evolves with your business.